{"data":{"id":"10.4121/ba5805cb-a909-4185-97b0-296739df7def","type":"dois","attributes":{"doi":"10.4121/ba5805cb-a909-4185-97b0-296739df7def","prefix":"10.4121","suffix":"ba5805cb-a909-4185-97b0-296739df7def","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Dong, Yongqi","nameType":"Personal","givenName":"Yongqi","familyName":"Dong","nameIdentifiers":[{"nameIdentifier":"0000-0003-1159-9584","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Patil,  Sandeep","givenName":" Sandeep","familyName":"Patil","affiliation":[],"nameIdentifiers":[]},{"name":"van Arem, Bart","givenName":"Bart","familyName":"van Arem","affiliation":[],"nameIdentifiers":[]},{"name":"Haneen Farah","nameType":"Personal","affiliation":[],"nameIdentifiers":[{"nameIdentifier":"0000-0002-2919-0253","nameIdentifierScheme":"https://orcid.org/"}]}],"titles":[{"title":"Code underlying the publication: A Hybrid Spatial-temporal Deep Learning Architecture for Lane Detection"}],"publisher":"4TU.ResearchData","container":{},"publicationYear":2025,"subjects":[{"subject":"Artificial Intelligence and Image Processing","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0801"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Ground Transport","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"8801"},{"subject":"Other Technology","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"1099"},{"subject":"FOS: Other engineering and technologies","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Transport","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"88"},{"subject":"Technology","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"10"},{"subject":"Information and Computing Sciences","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"08"},{"subject":"Lane detection"},{"subject":"Deep learning"},{"subject":"Computer vision"},{"subject":"Automated driving"},{"subject":"Deep neural network"},{"subject":"CNN"},{"subject":"RNN"},{"subject":"ConvLSTM"}],"contributors":[{"name":"Delft University Of Technology, Faculty Of Civil Engineering And Geosciences, Department Of Transport And Planning","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[]}],"dates":[{"date":"2025-02-20","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1111/mice.12829","relatedIdentifierType":"DOI"},{"relationType":"IsVersionOf","relatedIdentifier":"10.4121/ba5805cb-a909-4185-97b0-296739df7def.v1","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":["py; txt; jpg; avi"],"version":"1","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Codes for the paper\u003cstrong\u003eDong, Y.\u003c/strong\u003e, Patil, S., van Arem, B., \u0026amp; Farah, H. (2023). A Hybrid Spatial-temporal Deep Learning Architecture for Lane Detection. \u003cem\u003eComputer-Aided Civil and Infrastructure Engineering\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(1), pp.67–86.\u003cbr\u003eAccurate and reliable lane detection is vital for the safe performance of lane-keeping assistance and lane departure warning systems. However, under certain challenging circumstances, it is difficult to get satisfactory performance in accurately detecting the lanes from one single image as mostly done in current literature. Since lane markings are continuous lines, the lanes that are difficult to be accurately detected in the current single image can potentially be better deduced if information from previous frames is incorporated. This study proposes a novel hybrid spatial–temporal (ST) sequence-to-one deep learning architecture. This architecture makes full use of the ST information in multiple continuous image frames to detect the lane markings in the very last frame. Specifically, the hybrid model integrates the following aspects: (a) the single image feature extraction module equipped with the spatial convolutional neural network; (b) the ST feature integration module constructed by ST recurrent neural network; (c) the encoder–decoder structure, which makes this image segmentation problem work in an end-to-end supervised learning format. Extensive experiments reveal that the proposed model architecture can effectively handle challenging driving scenes and outperforms available state-of-the-art methods.\u003cbr\u003e","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"awardTitle":"Safe and efficient operation of AutoMated and human drivEN vehicles in mixed traffic","funderName":"Applied and Technical Sciences (TTW), a subdomain of the Dutch Institute for Scientific Research 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